Genetics

A review of deep learning applications in human genomics using next-generation sequencing data.

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep lear...

Benchmarking structural evolution methods for training of machine learned interatomic potentials.

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional ...

Accurate somatic variant detection using weakly supervised deep learning.

Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop V...

IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, how...

Comparative Analysis of Aesthetic Emotion of Dance Movement: A Deep Learning Based Approach.

Dance is a unique art with the human body movement as the main means, but dance is not limited to the human body movement itself. Like any art, dance ...

Teaching Analysis for Visual Communication Design with the Perspective of Digital Technology.

The turn of contemporary visual culture has led to the expansion of the connotation and scope of visual communication design (VCD) education, the gene...

On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request.

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P sy...

Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction.

Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for...

Leveraging a Neuroevolutionary Approach for Classifying Violent Behavior in Video.

Security has become a critical issue for complex and expensive systems and day-to-day situations. In this regard, the analysis of surveillance cameras...

Explainable Artificial Intelligence-Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine-Tuned CNN-Based Transfer Learning Model.

Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It i...

Machine Learning Approaches to TCR Repertoire Analysis.

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dram...

BERT-Promoter: An improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection.

A promoter is a sequence of DNA that initializes the process of transcription and regulates whenever and wherever genes are expressed in the organism....

Non-iterative learning machine for identifying CoViD19 using chest X-ray images.

CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this vi...

Moving closer to experimental level materials property prediction using AI.

While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, ex...

Deep Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease.

Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious a...

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical m...

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these...

Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.

The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human...

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation.

BACKGROUND: DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome witho...

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations, and Experiences.

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personal...